DNA methylation & adversity: pathways from exposures to health inequities

项目摘要 实现PAR-16-355的目标“扩大理解表观遗传机制的方法 社会因素导致影响健康差异的生物学变化，”我们的研究将进行新的 基于群体的发现和复制分析，以研究DNA甲基化（DNAm）如何变化 面临种族歧视、经济困难和空气污染，以及对不平等的影响 心脏代谢疾病风险和加速老化（定义为表观遗传年龄>实际年龄）。 在发现阶段，我们将从我们的“我的身体，我的故事”中储存的血点中重新获得DNA。 （MBMS）研究（R 01 AG 027122），其参与者包括1005名美国出生的非 西班牙裔黑人（n = 504）和非西班牙裔白色（n = 501）成人，年龄35至64岁，从四个招募 波士顿社区卫生中心，MA（2008-2010）。本研究报告在以下方面有丰富的数据：（a）种族歧视 和社会经济地位，每一个都是在多个层面和整个生命过程中测量的，再加上住宅空气 过去一年的污染;（B）心脏代谢结果;（c）相关协变量。对于复制，我们将 使用来自Wave 5（2010-2012）子集的新可用的类似DNAm、暴露和健康数据 多种族动脉粥样硬化研究（梅萨）的参与者（N = 1264，年龄55-94; 582名非西班牙裔 白色，270名非西班牙裔黑人; 404名西班牙裔，来自马里兰州的巴尔的摩、北卡罗来纳州的福赛斯县、纽约市、纽约州和圣路易斯。 Paul，MN）。为了加强基于横截面数据的因果推理，我们将使用MR-Base， 最近构建了一个非常强大的应用程序，利用公开的全基因组关联 研究（GWAS）总结数据和孟德尔随机化（MR），以评估相关性的因果方向， 暴露、DNA m和健康结果之间的关系。因此，我们的具体目标是： 目标1：进行新的分析，以确定与以下因素相关的DNAm变异：（1）目标1.1：暴露于 种族歧视、经济困难和空气污染;（2）目标1.2：测量心脏代谢 结果（血压、空腹胰岛素、2型糖尿病、空腹心血管疾病10年 风险评分，代谢综合征）;加（3）目标1.3：对于在目标1.3中观察到的DNAm-健康结果相关性， 1.2，使用MR-Base来加强关于关联方向的因果推断（例如，甲基化 原因与疾病所致）;发现分析：MBMS;复制分析：梅萨。 目标2：评估目标1研究暴露与健康结果之间的关系， 加速老化（表观遗传年龄>实足年龄），如三个新鉴定的DNA m “时钟”（Horvath，Hannum和DNAm PhenoAge）;发现：MBMS;复制：梅萨。 目的3：分析甲基化位点和“时钟”是否与研究暴露和健康相关 结果介导了这些保健-结果关联，从而助长了保健不平等。 影响：结果将促进有关DNA机制的知识，有助于健康差距。

Project Summary Addressing the objective of PAR-16-355 to “expand approaches for understanding epigenetic mechanisms by which social factors lead to biological changes that affect health disparities,” our study will conduct novel population-based discovery and replication analyses to investigate how DNA methylation (DNAm) varies with exposure to racial discrimination, economic hardship, and air pollution, and implications for disparities in cardiometabolic disease risk and accelerated aging (defined as epigenetic age > chronological age). For the discovery phase, we will newly obtain DNAm from the stored blood spots of our My Body, My Story (MBMS) study (R01 AG027122), whose participants comprise a random sample of 1005 US-born non- Hispanic black (n = 504) and non-Hispanic white (n = 501) adults, aged 35 to 64 years, recruited from four community health centers in Boston, MA (2008-2010). This study has rich data on: (a) racial discrimination and socioeconomic position, each measured at multiple levels and across the life-course, plus residential air pollution in the past year; (b) cardiometabolic outcomes; and (c) relevant covariates. For replication, we will use the newly available analogous DNAm, exposure, and health data from a subset of Wave 5 (2010-2012) participants in the Multi-Ethnic Study of Atherosclerosis (MESA) (N = 1264, age 55-94; 582 non-Hispanic white, 270 non-Hispanic black; 404 Hispanic, from Baltimore, MD, Forsyth County, NC, NYC, NY, and St. Paul, MN). To strengthen causal inference based on the cross-sectional data, we will employ MR-Base, a recently constructed highly powerful application that harnesses publicly-available genome-wide association study (GWAS) summary data and Mendelian Randomization (MR) to assess causal directions of associa- tions between exposures, DNAm, and health outcomes. Our Specific Aims thus are: Aim 1: Conduct novel analyses to identify variation in DNAm associated with: (1) Aim 1.1: exposure to racial discrimination, economic hardship, and air pollution; and (2) Aim 1.2: measured cardiometabolic outcomes (blood pressure, fasting insulin, Type 2 diabetes, Framingham Cardiovascular Disease 10-year risk score, metabolic syndrome); plus (3) Aim 1.3: For DNAm-health outcome associations observed in Aim 1.2, use MR-Base to strengthen causal inference about the direction of the associations (e.g., methylation causes vs. is due to disease); discovery analyses: MBMS; replication analyses: MESA. Aim 2: Assess the relationships between both the Aim 1 study exposures and health outcomes with accelerated aging (epigenetic age > chronological age), as identified by three newly identified DNAm “clocks” (Horvath, Hannum, and DNAm PhenoAge); discovery: MBMS; replication: MESA. Aim 3: Analyze if the methylation sites and “clocks” associated with both the study exposures and health outcomes mediate these exposure-outcomes associations, thereby contributing to health inequities. Impact: Results will advance knowledge about DNAm mechanisms contributing to health disparities.